Tree ensembles achieve state-of-the-art performance despite being greedily
optimized. Global refinement (GR) reduces greediness by jointly and globally
optimizing all constant leaves. We propose Joint Optimization of Piecewise
Linear ENsembles (JOPLEN), a piecewise-linear extension of GR. Compared to GR,
JOPLEN improves model flexibility and can apply common penalties, including
sparsity-promoting matrix norms and subspace-norms, to nonlinear prediction. We
evaluate the Frobenius norm, $\ell_{2,1}$ norm, and Laplacian regularization
for 146 regression and classification datasets; JOPLEN, combined with GB trees
and RF, achieves superior performance in both settings. Additionally, JOPLEN
with a nuclear norm penalty empirically learns smooth and subspace-aligned
functions. Finally, we perform multitask feature selection by extending the
Dirty LASSO. JOPLEN Dirty LASSO achieves a superior feature
sparsity/performance tradeoff to linear and gradient boosted approaches. We
anticipate that JOPLEN will improve regression, classification, and feature
selection across many fields.

Tree ensembles and Joint Optimization of Piecewise Linear ENsembles (JOPLEN) achieve superior performance in regression, classification, and feature selection, employing global refinement, penalties, and multitask feature selection.

分段线性集成的联合优化

Joint Optimization of Piecewise Linear Ensembles

We present a novel, end-to-end learnable, multiview 3D point cloud
registration algorithm. Registration of multiple scans typically follows a
two-stage pipeline: the initial pairwise alignment and the globally consistent
refinement. The former is often ambiguous due to the low overlap of neighboring
point clouds, symmetries and repetitive scene parts. Therefore, the latter
global refinement aims at establishing the cyclic consistency across multiple
scans and helps in resolving the ambiguous cases. In this paper we propose, to
the best of our knowledge, the first end-to-end algorithm for joint learning of
both parts of this two-stage problem. Experimental evaluation on well accepted
benchmark datasets shows that our approach outperforms the state-of-the-art by
a significant margin, while being end-to-end trainable and computationally less
costly. Moreover, we present detailed analysis and an ablation study that
validate the novel components of our approach. The source code and pretrained
models are publicly available under
this https URL

本文介绍了一种新颖的端到端可学习的多视角三维点云拼接算法，该算法集成了两阶段流程（成对对准和全局优化），并且是当前领先技术的改进。